Incorporating Automated Vehicle Identification Data into Origin-Destination Estimation

2004 ◽  
Vol 1882 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Constantinos Antoniou ◽  
Moshe Ben-Akiva ◽  
Haris N. Koutsopoulos
Keyword(s):  
Automated Vehicle ◽  
Vehicle Identification

Dynamic Origin-Destination Matrix Estimation from Traffic Counts and Automated Vehicle Identification Data

1997 ◽  
Vol 1607 (1) ◽  
pp. 87-94 ◽  
Author(s):  
Nanne J. Van Der Zijpp
Keyword(s):  
Synthetic Data ◽  
Inequality Constraints ◽  
Bayesian Updating ◽  
Problem Definition ◽  
Superior Performance ◽  
License Plate ◽  
Traffic Counts ◽  
Matrix Estimation ◽  
Automated Vehicle ◽  
Vehicle Identification

The problem of estimating time-varying origin-destination matrices from time series of traffic counts is extended to allow for the use of partial vehicle trajectory observations. These may be obtained by using automated vehicle identification (AVI), for example, automated license plate recognition, but they may also originate from floating car data. The central problem definition allows for the use of data from induction loops and AVI equipment at arbitrary (but fixed) locations and allows for the presence of random error in traffic counts and misrecognition at the AVI stations. Although the described methods may be extended to more complex networks, the application addressed involves a single highway corridor in which no route choice alternatives exist. Analysis of the problem leads to an expression for the mutual dependencies between link volume observations and AVI data and the formulation of an estimation problem with inequality constraints. A number of traditional estimation procedures such as discounted constrained least squares (DCLS) and the Kalman filter are described, and a new procedure referred to as Bayesian updating is proposed. The advantage of this new procedure is that it deals with the inequality constraints in an appropriate statistical manner. Experiments with a large number of synthetic data sets indicate in all cases a reduction of the error of estimation due to usage of trajectory counts and, compared with the traditional DCLS and Kalman filtering methods, a superior performance of the Bayesian updating procedure.

A Comparative Study between Private-Sector and Automated Vehicle Identification System Data through Various Travel Time Reliability Measures

2018 ◽  
Vol 2672 (42) ◽  
pp. 103-114
Author(s):  
Whoibin Chung ◽  
Mohamed Abdel-Aty ◽  
Juneyoung Park ◽  
Raj Ponnaluri
Keyword(s):  
Private Sector ◽  
Travel Time ◽  
Time Of Day ◽  
Traffic Information ◽  
Traffic Data ◽  
Travel Time Reliability ◽  
Automated Vehicle ◽  
Vehicle Identification ◽  
Day Of The Week ◽  
Time Periods

Traffic data from private-sector sources is increasingly used to estimate the travel time reliability of major road infrastructure. However, there is as yet no study evaluating the difference in estimating travel time reliability between the private-sector data and automated vehicle identification (AVI) based on radio frequency identification. As ground truth data, the AVI data were collected from an AVI system using toll tags and aggregated into five-minute intervals. As one of the representative traffic information providers, data from HERE was obtained through the Regional Integrated Traffic Information System, calculated in five-minute intervals. For the comparison, four kinds of measures were selected and estimated on the basis of the day of the week, specific time periods, and time of day in five-minute, 15-minute, and one-hour intervals. The statistical difference in travel time reliability was assessed through paired t-tests. According to the results, AVI and HERE data are comparable based on day of the week, specific time periods, and time of day at one-hour intervals, whereas at five-minute and 15-minute intervals, HERE and AVI data are not generally comparable. Thus, when estimating travel time reliability in real time, travel time reliability derived from HERE data may be different from the true travel time reliability. Considering that private-sector traffic data are currently used to estimate travel time reliability measures, the measures should be harmonized on the basis of robust statistics to provide more consistent measures related to the true travel time reliability.

Dynamic OD Estimation under Automated Vehicle Identification Environment

2011 ◽  
Vol 467-469 ◽  
pp. 835-840
Author(s):  
Da Shan Chen ◽  
Jian Sun ◽  
Ke Ping Li
Keyword(s):  
Traffic Control ◽  
Theoretical Models ◽  
State Equation ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Od Estimation ◽  
Automated Vehicle ◽  
Vehicle Identification ◽  
Estimation Problems ◽  
Control And Management

In order to solve the current dynamic OD estimation problems on the background of gradual application of automated vehicle identification facilities, the relationship between dynamic OD estimation and traffic parameters under AVI environment is analyzed. The dynamic OD estimation model basing on Kalman filter algorithm is established. The coefficient matrixes of state equation and observation equation are calibrated dynamically by neural network respectively. The simulation results show that the model has higher estimation accuracy for OD pairs with great flows. The model can be adopted as one of the theoretical models for dynamic OD estimation supporting traffic control and management.

scholarly journals Genetic algorithms approach to the problem of the automated vehicle identification equipment locations

2002 ◽  
Vol 36 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Dusan Teodorovic ◽  
Michel van Aerde ◽  
Fulin Zhu ◽  
Francois Dion
Keyword(s):  
Genetic Algorithms ◽  
Automated Vehicle ◽  
Vehicle Identification

Driver Back-Tracing Based on Automated Vehicle Identification Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 84-93
Author(s):  
Jian Yuan ◽  
Chunhui Yu ◽  
Ling Wang ◽  
Wanjing Ma
Keyword(s):  
Public Transportation ◽  
Traffic Congestion ◽  
Model Performance ◽  
Economic Loss ◽  
Sensitivity Analyses ◽  
Automated Vehicle ◽  
Congestion Mitigation ◽  
Vehicle Identification ◽  
Traffic Volumes ◽  
Temporal Dimensions

Traffic congestion causes traveler delay, environmental deterioration, and economic loss. Most studies on congestion mitigation focus on attracting travelers to public transportation and expanding road capacity. Few studies have been found to analyze the contribution of different traffic flows to the congestion on roads of interest. This study proposes an approach to driver back-tracing on the basis of automated vehicle identification (AVI) data for congestion mitigation. Driver back-tracing (DBT) aims to estimate the sources of the vehicles on roads of interest in both spatial and temporal dimensions. The spatial DBT model identifies the origins of vehicles on the roads and the temporal DBT model estimates the travel time from the origins to the roads. The difficulty lies in that vehicle trajectories are incomplete because of the low coverage of AVI detectors. Deep neural network classification and regression are applied to the spatial and temporal DBT models, respectively. Simulation data from VISSIM are collected as the dataset because of the lack of field data. Numerical studies validate the promising application and advantages of deep neural networks for the DBT problems. Sensitivity analyses show that the proposed models are robust to traffic volumes. However, turning ratios, and the number and layout of AVI detectors may have noticeable impacts on the model performance.

Alternate Trajectory Determination Using Multimode Automated Robotic Vehicle with Line Following and Object Following Mode

2018 ◽  
Vol 8 (3) ◽  
pp. 46
Author(s):  
Varun Kumar ◽  
Lakshya Gaur ◽  
Arvind Rehalia
Keyword(s):  
Ultrasonic Sensor ◽  
Robotic Assembly ◽  
Voltage Regulator ◽  
Automated Vehicle ◽  
Dc Motors ◽  
Arduino Uno ◽  
Robotic Vehicle ◽  
Line Following ◽  
Selection Of ◽  
Trajectory Determination

In this paper the authors have explained the development of robotic vehicle prepared by them, which operates autonomously and is not controlled by the users, except for selection of modes. The different modes of the automated vehicle are line following, object following and object avoidance with alternate trajectory determination. The complete robotic assembly is mounted on a chassis comprising of Arduino Uno, Servo motors, HC-SRO4 (Ultrasonic sensor), DC motors (Geared), L293D Motor Driver, IR proximity sensors, Voltage Regulator along with castor wheel and two normal wheels.

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